CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry
This provides an efficient and accurate solution for radar-based localization in robotics, though it is incremental as it builds on existing filtering and scan matching techniques.
The paper tackles large-scale radar odometry by introducing CFEAR Radarodometry, a learning-free method that achieves an overall translation error of 1.76% in a public urban benchmark and runs at 55Hz on a single laptop CPU thread.
This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.